{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-21 18:18:22.316620: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-06-21 18:18:22.346312: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-06-21 18:18:22.346332: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-06-21 18:18:22.346997: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-06-21 18:18:22.351764: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-21 18:18:23.842137: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5818 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3080, pci bus id: 0000:1a:00.0, compute capability: 8.6\n",
      "2024-06-21 18:18:23.842600: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 8019 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 3080, pci bus id: 0000:68:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94765736/94765736 [==============================] - 1s 0us/step\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                Output Shape                 Param #   Connected to                  \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)        [(None, 128, 128, 3)]        0         []                            \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)   (None, 134, 134, 3)          0         ['input_1[0][0]']             \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)         (None, 64, 64, 64)           9472      ['conv1_pad[0][0]']           \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalizati  (None, 64, 64, 64)           256       ['conv1_conv[0][0]']          \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)     (None, 64, 64, 64)           0         ['conv1_bn[0][0]']            \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)   (None, 66, 66, 64)           0         ['conv1_relu[0][0]']          \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)   (None, 32, 32, 64)           0         ['pool1_pad[0][0]']           \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2  (None, 32, 32, 64)           4160      ['pool1_pool[0][0]']          \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2  (None, 32, 32, 64)           36928     ['conv2_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2  (None, 32, 32, 256)          16640     ['pool1_pool[0][0]']          \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2  (None, 32, 32, 256)          16640     ['conv2_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv2_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv2_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)      (None, 32, 32, 256)          0         ['conv2_block1_0_bn[0][0]',   \n",
      "                                                                     'conv2_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activati  (None, 32, 32, 256)          0         ['conv2_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2  (None, 32, 32, 64)           16448     ['conv2_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2  (None, 32, 32, 64)           36928     ['conv2_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2  (None, 32, 32, 256)          16640     ['conv2_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv2_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)      (None, 32, 32, 256)          0         ['conv2_block1_out[0][0]',    \n",
      "                                                                     'conv2_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activati  (None, 32, 32, 256)          0         ['conv2_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2  (None, 32, 32, 64)           16448     ['conv2_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2  (None, 32, 32, 64)           36928     ['conv2_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNo  (None, 32, 32, 64)           256       ['conv2_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activ  (None, 32, 32, 64)           0         ['conv2_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2  (None, 32, 32, 256)          16640     ['conv2_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNo  (None, 32, 32, 256)          1024      ['conv2_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)      (None, 32, 32, 256)          0         ['conv2_block2_out[0][0]',    \n",
      "                                                                     'conv2_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activati  (None, 32, 32, 256)          0         ['conv2_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2  (None, 16, 16, 128)          32896     ['conv2_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2  (None, 16, 16, 128)          147584    ['conv3_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2  (None, 16, 16, 512)          131584    ['conv2_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2  (None, 16, 16, 512)          66048     ['conv3_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNo  (None, 16, 16, 512)          2048      ['conv3_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNo  (None, 16, 16, 512)          2048      ['conv3_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)      (None, 16, 16, 512)          0         ['conv3_block1_0_bn[0][0]',   \n",
      "                                                                     'conv3_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activati  (None, 16, 16, 512)          0         ['conv3_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2  (None, 16, 16, 128)          65664     ['conv3_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2  (None, 16, 16, 128)          147584    ['conv3_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2  (None, 16, 16, 512)          66048     ['conv3_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNo  (None, 16, 16, 512)          2048      ['conv3_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)      (None, 16, 16, 512)          0         ['conv3_block1_out[0][0]',    \n",
      "                                                                     'conv3_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activati  (None, 16, 16, 512)          0         ['conv3_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2  (None, 16, 16, 128)          65664     ['conv3_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2  (None, 16, 16, 128)          147584    ['conv3_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2  (None, 16, 16, 512)          66048     ['conv3_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNo  (None, 16, 16, 512)          2048      ['conv3_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)      (None, 16, 16, 512)          0         ['conv3_block2_out[0][0]',    \n",
      "                                                                     'conv3_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activati  (None, 16, 16, 512)          0         ['conv3_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2  (None, 16, 16, 128)          65664     ['conv3_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block4_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block4_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2  (None, 16, 16, 128)          147584    ['conv3_block4_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNo  (None, 16, 16, 128)          512       ['conv3_block4_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activ  (None, 16, 16, 128)          0         ['conv3_block4_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2  (None, 16, 16, 512)          66048     ['conv3_block4_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNo  (None, 16, 16, 512)          2048      ['conv3_block4_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)      (None, 16, 16, 512)          0         ['conv3_block3_out[0][0]',    \n",
      "                                                                     'conv3_block4_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activati  (None, 16, 16, 512)          0         ['conv3_block4_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2  (None, 8, 8, 256)            131328    ['conv3_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2  (None, 8, 8, 1024)           525312    ['conv3_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block1_0_bn[0][0]',   \n",
      "                                                                     'conv4_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2  (None, 8, 8, 256)            262400    ['conv4_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block1_out[0][0]',    \n",
      "                                                                     'conv4_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2  (None, 8, 8, 256)            262400    ['conv4_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block2_out[0][0]',    \n",
      "                                                                     'conv4_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2  (None, 8, 8, 256)            262400    ['conv4_block3_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block4_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block4_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block4_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block4_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block4_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block4_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block4_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block3_out[0][0]',    \n",
      "                                                                     'conv4_block4_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block4_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2  (None, 8, 8, 256)            262400    ['conv4_block4_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block5_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block5_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block5_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block5_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block5_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block5_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block5_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block4_out[0][0]',    \n",
      "                                                                     'conv4_block5_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block5_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2  (None, 8, 8, 256)            262400    ['conv4_block5_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block6_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block6_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2  (None, 8, 8, 256)            590080    ['conv4_block6_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNo  (None, 8, 8, 256)            1024      ['conv4_block6_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activ  (None, 8, 8, 256)            0         ['conv4_block6_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2  (None, 8, 8, 1024)           263168    ['conv4_block6_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNo  (None, 8, 8, 1024)           4096      ['conv4_block6_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)      (None, 8, 8, 1024)           0         ['conv4_block5_out[0][0]',    \n",
      "                                                                     'conv4_block6_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activati  (None, 8, 8, 1024)           0         ['conv4_block6_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2  (None, 4, 4, 512)            524800    ['conv4_block6_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block1_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block1_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2  (None, 4, 4, 512)            2359808   ['conv5_block1_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block1_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block1_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2  (None, 4, 4, 2048)           2099200   ['conv4_block6_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2  (None, 4, 4, 2048)           1050624   ['conv5_block1_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNo  (None, 4, 4, 2048)           8192      ['conv5_block1_0_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNo  (None, 4, 4, 2048)           8192      ['conv5_block1_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)      (None, 4, 4, 2048)           0         ['conv5_block1_0_bn[0][0]',   \n",
      "                                                                     'conv5_block1_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activati  (None, 4, 4, 2048)           0         ['conv5_block1_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2  (None, 4, 4, 512)            1049088   ['conv5_block1_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block2_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block2_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2  (None, 4, 4, 512)            2359808   ['conv5_block2_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block2_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block2_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2  (None, 4, 4, 2048)           1050624   ['conv5_block2_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNo  (None, 4, 4, 2048)           8192      ['conv5_block2_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)      (None, 4, 4, 2048)           0         ['conv5_block1_out[0][0]',    \n",
      "                                                                     'conv5_block2_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activati  (None, 4, 4, 2048)           0         ['conv5_block2_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2  (None, 4, 4, 512)            1049088   ['conv5_block2_out[0][0]']    \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block3_1_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block3_1_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2  (None, 4, 4, 512)            2359808   ['conv5_block3_1_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNo  (None, 4, 4, 512)            2048      ['conv5_block3_2_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activ  (None, 4, 4, 512)            0         ['conv5_block3_2_bn[0][0]']   \n",
      " ation)                                                                                           \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2  (None, 4, 4, 2048)           1050624   ['conv5_block3_2_relu[0][0]'] \n",
      " D)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNo  (None, 4, 4, 2048)           8192      ['conv5_block3_3_conv[0][0]'] \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)      (None, 4, 4, 2048)           0         ['conv5_block2_out[0][0]',    \n",
      "                                                                     'conv5_block3_3_bn[0][0]']   \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activati  (None, 4, 4, 2048)           0         ['conv5_block3_add[0][0]']    \n",
      " on)                                                                                              \n",
      "                                                                                                  \n",
      " global_average_pooling2d (  (None, 2048)                 0         ['conv5_block3_out[0][0]']    \n",
      " GlobalAveragePooling2D)                                                                          \n",
      "                                                                                                  \n",
      " dense (Dense)               (None, 512)                  1049088   ['global_average_pooling2d[0][\n",
      "                                                                    0]']                          \n",
      "                                                                                                  \n",
      " dropout (Dropout)           (None, 512)                  0         ['dense[0][0]']               \n",
      "                                                                                                  \n",
      " dense_1 (Dense)             (None, 64)                   32832     ['dropout[0][0]']             \n",
      "                                                                                                  \n",
      " dropout_1 (Dropout)         (None, 64)                   0         ['dense_1[0][0]']             \n",
      "                                                                                                  \n",
      " dense_2 (Dense)             (None, 5)                    325       ['dropout_1[0][0]']           \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 24669957 (94.11 MB)\n",
      "Trainable params: 24616837 (93.91 MB)\n",
      "Non-trainable params: 53120 (207.50 KB)\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.applications import (\n",
    "    ResNet50, VGG16, MobileNetV2, InceptionV3, DenseNet121, Xception, NASNetMobile\n",
    ")\n",
    "from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Dropout\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "# Define a function to load and modify a pretrained model\n",
    "def load_model(name: str, num_classes: int, input_shape=(128, 128, 3)) -> Model:\n",
    "    base_model = None\n",
    "\n",
    "    if name == \"resnet50\":\n",
    "        base_model = ResNet50(weights='imagenet', include_top=False, input_shape=input_shape)\n",
    "    elif name == \"vgg16\":\n",
    "        base_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)\n",
    "    elif name == \"mobilenet_v2\":\n",
    "        base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=input_shape)\n",
    "    elif name == \"inception_v3\":\n",
    "        base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=input_shape)\n",
    "    \n",
    "    else:\n",
    "        raise ValueError(f\"Model {name} not supported.\")\n",
    "\n",
    "    # Add custom layers on top of the base model\n",
    "    x = base_model.output\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Dense(512, activation='relu')(x)\n",
    "    x = Dropout(0.5)(x)\n",
    "    x = Dense(64, activation='relu')(x)\n",
    "    x = Dropout(0.5)(x)\n",
    "    predictions = Dense(num_classes, activation='softmax')(x)\n",
    "\n",
    "    # Create the full model\n",
    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "    return model\n",
    "\n",
    "# Example usage\n",
    "input_shape = (128, 128, 3)\n",
    "num_classes = 5  # Adjust according to your dataset\n",
    "\n",
    "model = load_model(\"resnet50\", num_classes, input_shape)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "\n",
    "# Print the model summary\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Load the dataset\n",
    "hdf5_file_path = 'subset_dataset.h5'\n",
    "\n",
    "with h5py.File(hdf5_file_path, 'r') as hdf5_file:\n",
    "    images = hdf5_file['images'][:]\n",
    "    labels = hdf5_file['labels'][:]\n",
    "\n",
    "# Split the data\n",
    "X_train, X_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=42)\n",
    "\n",
    "# Save the splits\n",
    "def save_hdf5(data, labels, filename):\n",
    "    with h5py.File(filename, 'w') as hdf5_file:\n",
    "        hdf5_file.create_dataset('images', data=data, compression='gzip')\n",
    "        hdf5_file.create_dataset('labels', data=labels, compression='gzip')\n",
    "\n",
    "save_hdf5(X_train, y_train, 'train_dataset.h5')\n",
    "save_hdf5(X_val, y_val, 'val_dataset.h5')\n",
    "save_hdf5(X_test, y_test, 'test_dataset.h5')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
      "Training model: resnet50\n",
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-21 18:30:51.373467: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8902\n",
      "2024-06-21 18:30:52.892282: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fe09c340790 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2024-06-21 18:30:52.892310: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA GeForce RTX 3080, Compute Capability 8.6\n",
      "2024-06-21 18:30:52.892313: I external/local_xla/xla/service/service.cc:176]   StreamExecutor device (1): NVIDIA GeForce RTX 3080, Compute Capability 8.6\n",
      "2024-06-21 18:30:52.896568: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1719019852.965863   81073 device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "242/242 [==============================] - ETA: 0s - loss: 0.3663 - accuracy: 0.8656"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/researchgroup/miniconda3/envs/newtest/lib/python3.11/site-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "242/242 [==============================] - 11s 30ms/step - loss: 0.3663 - accuracy: 0.8656 - val_loss: 0.1425 - val_accuracy: 0.9674\n",
      "Epoch 2/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1669 - accuracy: 0.9569 - val_loss: 0.1197 - val_accuracy: 0.9697\n",
      "Epoch 3/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1546 - accuracy: 0.9595 - val_loss: 0.1092 - val_accuracy: 0.9709\n",
      "Epoch 4/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1369 - accuracy: 0.9644 - val_loss: 0.1029 - val_accuracy: 0.9732\n",
      "Epoch 5/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1325 - accuracy: 0.9645 - val_loss: 0.1230 - val_accuracy: 0.9616\n",
      "Epoch 6/20\n",
      "242/242 [==============================] - 5s 21ms/step - loss: 0.1307 - accuracy: 0.9648 - val_loss: 0.0970 - val_accuracy: 0.9721\n",
      "Epoch 7/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1255 - accuracy: 0.9671 - val_loss: 0.1006 - val_accuracy: 0.9744\n",
      "Epoch 8/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1249 - accuracy: 0.9666 - val_loss: 0.1003 - val_accuracy: 0.9744\n",
      "Epoch 9/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1196 - accuracy: 0.9687 - val_loss: 0.0942 - val_accuracy: 0.9756\n",
      "Epoch 10/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1200 - accuracy: 0.9700 - val_loss: 0.0959 - val_accuracy: 0.9756\n",
      "Epoch 11/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1193 - accuracy: 0.9687 - val_loss: 0.0979 - val_accuracy: 0.9732\n",
      "Epoch 12/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1138 - accuracy: 0.9682 - val_loss: 0.0911 - val_accuracy: 0.9756\n",
      "Epoch 13/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1141 - accuracy: 0.9696 - val_loss: 0.0916 - val_accuracy: 0.9732\n",
      "Epoch 14/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1153 - accuracy: 0.9696 - val_loss: 0.0918 - val_accuracy: 0.9756\n",
      "Epoch 15/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1134 - accuracy: 0.9707 - val_loss: 0.0993 - val_accuracy: 0.9756\n",
      "Epoch 16/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1105 - accuracy: 0.9715 - val_loss: 0.0894 - val_accuracy: 0.9756\n",
      "Epoch 17/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1135 - accuracy: 0.9692 - val_loss: 0.0915 - val_accuracy: 0.9744\n",
      "Epoch 18/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1100 - accuracy: 0.9709 - val_loss: 0.1072 - val_accuracy: 0.9744\n",
      "Epoch 19/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1103 - accuracy: 0.9707 - val_loss: 0.0981 - val_accuracy: 0.9732\n",
      "Epoch 20/20\n",
      "242/242 [==============================] - 5s 20ms/step - loss: 0.1064 - accuracy: 0.9713 - val_loss: 0.0914 - val_accuracy: 0.9756\n",
      "68/68 [==============================] - 2s 24ms/step - loss: 0.1134 - accuracy: 0.9688\n",
      "resnet50 - Test loss: 0.1134\n",
      "resnet50 - Test accuracy: 0.9688\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model: vgg16\n",
      "Epoch 1/20\n",
      "242/242 [==============================] - 7s 26ms/step - loss: 0.2097 - accuracy: 0.9364 - val_loss: 0.1052 - val_accuracy: 0.9767\n",
      "Epoch 2/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1210 - accuracy: 0.9678 - val_loss: 0.0907 - val_accuracy: 0.9767\n",
      "Epoch 3/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1128 - accuracy: 0.9697 - val_loss: 0.0864 - val_accuracy: 0.9767\n",
      "Epoch 4/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1121 - accuracy: 0.9709 - val_loss: 0.1136 - val_accuracy: 0.9732\n",
      "Epoch 5/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1047 - accuracy: 0.9726 - val_loss: 0.0868 - val_accuracy: 0.9767\n",
      "Epoch 6/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1003 - accuracy: 0.9731 - val_loss: 0.0917 - val_accuracy: 0.9767\n",
      "Epoch 7/20\n",
      "242/242 [==============================] - 5s 23ms/step - loss: 0.0996 - accuracy: 0.9729 - val_loss: 0.0839 - val_accuracy: 0.9790\n",
      "Epoch 8/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.0972 - accuracy: 0.9732 - val_loss: 0.0889 - val_accuracy: 0.9790\n",
      "Epoch 9/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.0998 - accuracy: 0.9742 - val_loss: 0.0883 - val_accuracy: 0.9767\n",
      "Epoch 10/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.0956 - accuracy: 0.9731 - val_loss: 0.0860 - val_accuracy: 0.9779\n",
      "Epoch 11/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.0954 - accuracy: 0.9737 - val_loss: 0.0847 - val_accuracy: 0.9790\n",
      "Epoch 12/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.0937 - accuracy: 0.9744 - val_loss: 0.0900 - val_accuracy: 0.9779\n",
      "68/68 [==============================] - 1s 21ms/step - loss: 0.1037 - accuracy: 0.9678\n",
      "vgg16 - Test loss: 0.1037\n",
      "vgg16 - Test accuracy: 0.9678\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model: mobilenet_v2\n",
      "Epoch 1/20\n",
      "242/242 [==============================] - 7s 19ms/step - loss: 0.2262 - accuracy: 0.9394 - val_loss: 0.0873 - val_accuracy: 0.9814\n",
      "Epoch 2/20\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.1290 - accuracy: 0.9649 - val_loss: 0.0828 - val_accuracy: 0.9790\n",
      "Epoch 3/20\n",
      "242/242 [==============================] - 4s 14ms/step - loss: 0.1195 - accuracy: 0.9683 - val_loss: 0.0960 - val_accuracy: 0.9814\n",
      "Epoch 4/20\n",
      "242/242 [==============================] - 3s 14ms/step - loss: 0.1158 - accuracy: 0.9683 - val_loss: 0.0871 - val_accuracy: 0.9779\n",
      "Epoch 5/20\n",
      "242/242 [==============================] - 4s 14ms/step - loss: 0.1032 - accuracy: 0.9741 - val_loss: 0.0931 - val_accuracy: 0.9779\n",
      "Epoch 6/20\n",
      "242/242 [==============================] - 4s 14ms/step - loss: 0.1019 - accuracy: 0.9716 - val_loss: 0.0880 - val_accuracy: 0.9802\n",
      "Epoch 7/20\n",
      "242/242 [==============================] - 4s 15ms/step - loss: 0.0966 - accuracy: 0.9746 - val_loss: 0.0943 - val_accuracy: 0.9756\n",
      "68/68 [==============================] - 1s 12ms/step - loss: 0.1283 - accuracy: 0.9655\n",
      "mobilenet_v2 - Test loss: 0.1283\n",
      "mobilenet_v2 - Test accuracy: 0.9655\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training model: inception_v3\n",
      "Epoch 1/20\n",
      "242/242 [==============================] - 13s 36ms/step - loss: 0.4351 - accuracy: 0.9305 - val_loss: 0.1269 - val_accuracy: 0.9581\n",
      "Epoch 2/20\n",
      "242/242 [==============================] - 6s 23ms/step - loss: 0.1504 - accuracy: 0.9588 - val_loss: 0.1078 - val_accuracy: 0.9709\n",
      "Epoch 3/20\n",
      "242/242 [==============================] - 6s 23ms/step - loss: 0.1462 - accuracy: 0.9612 - val_loss: 0.1011 - val_accuracy: 0.9721\n",
      "Epoch 4/20\n",
      "242/242 [==============================] - 5s 21ms/step - loss: 0.1327 - accuracy: 0.9653 - val_loss: 0.1073 - val_accuracy: 0.9721\n",
      "Epoch 5/20\n",
      "242/242 [==============================] - 5s 21ms/step - loss: 0.1280 - accuracy: 0.9661 - val_loss: 0.1047 - val_accuracy: 0.9744\n",
      "Epoch 6/20\n",
      "242/242 [==============================] - 5s 21ms/step - loss: 0.1221 - accuracy: 0.9675 - val_loss: 0.1080 - val_accuracy: 0.9709\n",
      "Epoch 7/20\n",
      "242/242 [==============================] - 5s 21ms/step - loss: 0.1203 - accuracy: 0.9658 - val_loss: 0.1120 - val_accuracy: 0.9662\n",
      "Epoch 8/20\n",
      "242/242 [==============================] - 5s 22ms/step - loss: 0.1236 - accuracy: 0.9683 - val_loss: 0.1055 - val_accuracy: 0.9732\n"
     ]
    }
   ],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.applications import ResNet50, VGG16, MobileNetV2, InceptionV3\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout, Input\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "def build_pretrained_model(base_model, num_classes):\n",
    "    base_model.trainable = False  # Freeze the base model layers\n",
    "    x = base_model.output\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Dense(512, activation='relu')(x)\n",
    "    x = Dropout(0.5)(x)\n",
    "    outputs = Dense(num_classes, activation='softmax')(x)\n",
    "    model = Model(inputs=base_model.input, outputs=outputs)\n",
    "    return model\n",
    "\n",
    "def load_data(hdf5_file_path):\n",
    "    with h5py.File(hdf5_file_path, 'r') as hdf5_file:\n",
    "        images = hdf5_file['images'][:]\n",
    "        labels = hdf5_file['labels'][:]\n",
    "    return images, labels\n",
    "\n",
    "# Load data\n",
    "X_train, y_train = load_data('train_dataset.h5')\n",
    "X_val, y_val = load_data('val_dataset.h5')\n",
    "X_test, y_test = load_data('test_dataset.h5')\n",
    "\n",
    "# Define models\n",
    "pretrained_models = {\n",
    "    'resnet50': ResNet50(weights='imagenet', include_top=False, input_tensor=Input(shape=(128, 128, 3))),\n",
    "    'vgg16': VGG16(weights='imagenet', include_top=False, input_tensor=Input(shape=(128, 128, 3))),\n",
    "    'mobilenet_v2': MobileNetV2(weights='imagenet', include_top=False, input_tensor=Input(shape=(128, 128, 3))),\n",
    "    'inception_v3': InceptionV3(weights='imagenet', include_top=False, input_tensor=Input(shape=(128, 128, 3)))\n",
    "}\n",
    "\n",
    "num_classes = 5  # Replace with the actual number of classes in your dataset\n",
    "\n",
    "# Iterate over each model, train and save\n",
    "for model_name, base_model in pretrained_models.items():\n",
    "    print(f'Training model: {model_name}')\n",
    "    \n",
    "    model = build_pretrained_model(base_model, num_classes)\n",
    "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "    model_checkpoint = ModelCheckpoint(f'best_model_{model_name}.h5', monitor='val_loss', save_best_only=True)\n",
    "    \n",
    "    history = model.fit(\n",
    "        X_train, y_train,\n",
    "        validation_data=(X_val, y_val),\n",
    "        epochs=20,\n",
    "        batch_size=32,\n",
    "        callbacks=[early_stopping, model_checkpoint]\n",
    "    )\n",
    "    \n",
    "    # Load the best weights\n",
    "    model.load_weights(f'best_model_{model_name}.h5')\n",
    "    \n",
    "    # Evaluate on test data\n",
    "    test_loss, test_accuracy = model.evaluate(X_test, y_test)\n",
    "    print(f'{model_name} - Test loss: {test_loss:.4f}')\n",
    "    print(f'{model_name} - Test accuracy: {test_accuracy:.4f}')\n",
    "    \n",
    "    # Save the full model\n",
    "    model.save(f'final_model_{model_name}.h5')\n",
    "    \n",
    "    # Plot training and validation accuracy and loss\n",
    "    plt.figure(figsize=(12, 4))\n",
    "\n",
    "    # Accuracy plot\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['accuracy'], label='Train')\n",
    "    plt.plot(history.history['val_accuracy'], label='Validation')\n",
    "    plt.title(f'{model_name} - Model Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.legend()\n",
    "\n",
    "    # Loss plot\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['loss'], label='Train')\n",
    "    plt.plot(history.history['val_loss'], label='Validation')\n",
    "    plt.title(f'{model_name} - Model Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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